Incorporating predicted species distribution in adaptive and conventional sampling designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Monitoring rare and clustered populations is challenging because of the large effort required to encounter occupied habitat and yield precise population estimates (McDonald 2004). Sampling designs are available to help reduce the effort required to encounter occupied habitat and increase precision, including stratified sampling, probability proportional to size (PPS) sampling, and various adaptive sampling designs (Thompson 2002). Use of these designs is motivated, in an intuitive sense, by each design's ability to allocate more sampling effort where target species are (or are likely to be) and less where they are not. This intuitive approach to allocation of effort can lead to increased precision when variability in the population tends to be higher in areas of high species density or abundance (Box 17.1). Conventional designs, such as stratified and PPS sampling, rely on prior information to allocate effort. For example, prior information could come from predicted species or habitat distributions (Guisan and Zimmermann 2000, Le Lay et al. 2010). Use of prior information is not a basic property of adaptive sampling designs, but these designs could use such information when available.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it